Paper Citation Record · LEDGER

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation

As of 19 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2605.05249.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.05249 v3

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-01T00:12:24.019383Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T23:50:54.237809Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-06-29T23:54:03.371719Z

Reference resolution

56 of 56 outbound references displayed

verified exact16
verified fuzzy37
unresolved3
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4629212b-72a7-4a59-ac42-393a0edd256e · outbound

This paper cites Tran, Jonah Samost, Maciej Kula, Ed H.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Tran, Jonah Samost, Maciej Kula, Ed H

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.708241Z

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Observation 5e5f76cb-45d9-4905-a91b-c76f7e0e2c20 · outbound

This paper cites RecGPT Technical Report.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation RecGPT Technical Report

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.176885Z

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Observation 7ab003b8-d07d-4211-82eb-d6093dc61942 · outbound

This paper cites GME: Improving Universal Multimodal Retrieval by Multimodal LLMs.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation GME: Improving Universal Multimodal Retrieval by Multimodal LLMs

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:15:09.179248Z

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Observation 8f3522bd-999b-4bfa-a0ac-6654636f24c3 · outbound

This paper cites Lamra: Large multimodal model as your advanced retrieval assistant.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Lamra: Large multimodal model as your advanced retrieval assistant

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.701561Z

Source-reported events for the cited work

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Observation b13010cf-1daf-4b2d-a9b3-0a949ff14f24 · outbound

This paper cites Gpr: Towards a generative pre-trained one-model paradigm for large-scale advertising recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Gpr: Towards a generative pre-trained one-model paradigm for large-scale advertising recommendation

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.153832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e4293fb-bada-4dcd-9d14-ac1ebbe673ef · outbound

This paper cites ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:15:09.168863Z

Source-reported events for the cited work

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Observation 29acb1b1-5a8f-4cb0-8fee-0517aab48e38 · outbound

This paper cites CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:15:09.143148Z

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Observation 57e3c35e-a88f-45f5-b05f-fa06e1b7e1df · outbound

This paper cites Reasoning over Semantic IDs Enhances Generative Recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Reasoning over Semantic IDs Enhances Generative Recommendation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:15:09.151284Z

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Observation 63dc231a-d637-4af9-a169-6f818dfe0eae · outbound

This paper cites Chi, and Xinyang Yi.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Chi, and Xinyang Yi

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.706424Z

Source-reported events for the cited work

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Observation db0b92ec-1f3b-4f6b-bd9d-9b9ff976a951 · outbound

This paper cites Learnable item tokenization for generative recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Learnable item tokenization for generative recommendation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.694850Z

Source-reported events for the cited work

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Observation e288ae4f-323f-4d22-b159-c96879e833cf · outbound

This paper cites Session-based recommendation with graph neural networks.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Session-based recommendation with graph neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.696500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8ed12228-46a6-406e-b334-017cc46d2522 · outbound

This paper cites Recjpq: training large-catalogue sequential recom- menders.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Recjpq: training large-catalogue sequential recom- menders

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.691590Z

Source-reported events for the cited work

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Observation 92788c59-b921-490e-924d-235df8a604fa · outbound

This paper cites Learning vector-quantized item representation for transferable sequential recommenders.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Learning vector-quantized item representation for transferable sequential recommenders

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.698140Z

Source-reported events for the cited work

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Observation b1476bfb-a64c-4e46-88a5-79f5204342b9 · outbound

This paper cites Hyperman: Hypergraph- enhanced meta-learning adaptive network for next poi recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Hyperman: Hypergraph- enhanced meta-learning adaptive network for next poi recommendation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.699855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93c46620-a4ce-40c1-a31d-2f75a531bef2 · outbound

This paper cites OneRec-V2 Technical Report.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation OneRec-V2 Technical Report

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:15:09.158663Z

Source-reported events for the cited work

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Observation ebd1f1a3-86f1-4321-8319-badbccd76d88 · outbound

This paper cites A survey of generative recommendation from a tri-decoupled perspective: Tokenization, architecture, and optimization.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation A survey of generative recommendation from a tri-decoupled perspective: Tokenization, architecture, and optimization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.703283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 612e89d9-b25d-41f0-8570-579b52f07da2 · outbound

This paper cites Align 3gr: Unified multi-level alignment for llm-based generative recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Align 3gr: Unified multi-level alignment for llm-based generative recommendation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.679804Z

Source-reported events for the cited work

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Observation 6739c515-8aeb-4281-9928-f8664a354c34 · outbound

This paper cites Generating long semantic ids in parallel for recom- mendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Generating long semantic ids in parallel for recom- mendation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.676545Z

Source-reported events for the cited work

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Observation 1280aa35-43f7-45dd-a210-383a3f7b5969 · outbound

This paper cites Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5).

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.678149Z

Source-reported events for the cited work

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Observation e0d54455-f56b-40c7-bd06-c77e3172c3b7 · outbound

This paper cites A Survey of Generative Search and Recommendation in the Era of Large Language Models.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation A Survey of Generative Search and Recommendation in the Era of Large Language Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.161279Z

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Observation 054fb6c0-c629-4f0e-8f44-9a7a31bd7790 · outbound

This paper cites Generative large recommendation models: Emerging trends in llms for recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Generative large recommendation models: Emerging trends in llms for recommendation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.681641Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 64abbabe-db0b-4f41-9fd9-383f4115ad27 · outbound

This paper cites an unresolved cited work.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Unresolved cited work

Reference 22

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Observation a0e2cc92-ecdc-4b71-a62c-da48f054db3d · outbound

This paper cites Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.688299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f08b22f6-6dae-4e2e-920c-6a5e8a58b8a5 · outbound

This paper cites Mtgr: Industrial-scale generative recommendation framework in meituan.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Mtgr: Industrial-scale generative recommendation framework in meituan

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.671484Z

Source-reported events for the cited work

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Observation a36f631f-3e82-49ec-9ed9-06e62f659249 · outbound

This paper cites Longer: Scaling up long sequence modeling in industrial recommenders.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Longer: Scaling up long sequence modeling in industrial recommenders

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.686633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af576f6e-e5ed-48ab-a9f8-0d604547b91b · outbound

This paper cites Onetrans: Unified feature interaction and sequence modeling with one transformer in industrial recommender.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Onetrans: Unified feature interaction and sequence modeling with one transformer in industrial recommender

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.704952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a6bfc8f1-9ca4-42d5-bb72-d52ad5c90883 · outbound

This paper cites Towards Large-scale Generative Ranking.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Towards Large-scale Generative Ranking

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.140799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67eeb07f-c0d2-4b96-af4c-83548174f137 · outbound

This paper cites HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.163759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c30febf4-0f3b-4911-9cc0-c9501a06824c · outbound

This paper cites Unlocking scaling law in industrial recommendation systems with a three-step paradigm based large user model.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Unlocking scaling law in industrial recommendation systems with a three-step paradigm based large user model

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.656242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9bf4db0b-0b31-4707-87e4-5a3fe05a60a0 · outbound

This paper cites LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:15:09.156416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f88db0b3-2ed3-416d-8f28-cc05472b1f65 · outbound

This paper cites Optimized feature generation for tabular data via llms with decision tree reasoning.Advances in neural information processing systems, 37:92352–92380.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Optimized feature generation for tabular data via llms with decision tree reasoning.Advances in neural information processing systems, 37:92352–92380

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.650973Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13dc00c5-740b-4743-9645-9cf536b4918b · outbound

This paper cites Large language models make sample-efficient recommender systems.Frontiers of Computer Science, 19(4):194328.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Large language models make sample-efficient recommender systems.Frontiers of Computer Science, 19(4):194328

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.652728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82262106-6ba6-4758-bc35-9729c2557d1e · outbound

This paper cites Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.683384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1827eff-364a-481f-9938-8c93dd2f20cb · outbound

This paper cites Generative recommender with end-to-end learnable item tokenization.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Generative recommender with end-to-end learnable item tokenization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.654585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d13c433c-5945-45f5-9a44-f795cc03c309 · outbound

This paper cites Openonerec technical report.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Openonerec technical report

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.171576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a4827ee8-73aa-43bc-8c32-6d33fcccf479 · outbound

This paper cites Onesug: The unified end-to-end generative framework for e-commerce query suggestion.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Onesug: The unified end-to-end generative framework for e-commerce query suggestion

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.693276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ecdf87b-4b4b-4ace-9f1d-45ea6ac2a4c3 · outbound

This paper cites One model, two markets: Bid-aware generative recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation One model, two markets: Bid-aware generative recommendation

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.166411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a36ecea-23f0-4b01-808c-32a189ad4109 · outbound

This paper cites HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:15:09.148455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af595855-099a-4c5e-b8a4-e7ebf6f34a93 · outbound

This paper cites PixRec: Leveraging Visual Context for Next-Item Prediction in Sequential Recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation PixRec: Leveraging Visual Context for Next-Item Prediction in Sequential Recommendation

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-08-19T02:23:57.908547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 076d555f-85b8-430d-a655-f3332bd05109 · outbound

This paper cites Plum: Adapting pre-trained language models for industrial-scale generative recommendations.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Plum: Adapting pre-trained language models for industrial-scale generative recommendations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.642072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59abc1c1-3d4f-44a6-8c85-1af05dfbebca · outbound

This paper cites Entire space multi-task model: An effective approach for estimating post-click conversion rate.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Entire space multi-task model: An effective approach for estimating post-click conversion rate

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.643748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e0cfa716-7cb7-4c19-bad4-611a150b93b8 · outbound

This paper cites Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.645631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 15532603-ccdb-4305-803a-e262c063f020 · outbound

This paper cites Mmoe: Enhancing multimodal models with mixtures of multi- modal interaction experts.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Mmoe: Enhancing multimodal models with mixtures of multi- modal interaction experts

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.649238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4c80f27-91a9-43c4-87a2-381aa422188c · outbound

This paper cites Residual multi-task learner for applied ranking.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Residual multi-task learner for applied ranking

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.647401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 85d41ca7-7307-4253-b9fb-62933a031397 · outbound

This paper cites Hinet: Novel multi-scenario & multi-task learning with hierarchical information extraction.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Hinet: Novel multi-scenario & multi-task learning with hierarchical information extraction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.658011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db1eb422-9d13-40cb-842c-756baeaa967d · outbound

This paper cites Minionerec: An open-source framework for scaling generative recommendation.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Minionerec: An open-source framework for scaling generative recommendation

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.145952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9981e77c-ebd3-4eb8-be91-03cecf948dca · outbound

This paper cites McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.673171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1197c475-88db-4461-b6d8-f236a2cabbdc · outbound

This paper cites switch modes.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation switch modes

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.674785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e22459ca-eb62-4dda-ae62-70b0bc80fc63 · outbound

This paper cites an unresolved cited work.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-07-07T09:13:35.666397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd72e694-828e-4b31-a4b4-bedaca68c19f · outbound

This paper cites an unresolved cited work.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-07-07T09:13:35.689963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f13a4c4-62a3-47fe-9d87-b869142e5050 · outbound

This paper cites We then train the RQ-V AE tokenizer offline with 3 quantization levels and codebook sizes of 4096, 2048, and 1024, and generate fixed SID targets for all items.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation We then train the RQ-V AE tokenizer offline with 3 quantization levels and codebook sizes of 4096, 2048, and 1024, and generate fixed SID targets for all items

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.668098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d0d751d-53e7-44f9-91ae-e3c8c583809f · outbound

This paper cites The CMSA captions, MDIM interests, and SID targets are cached offline and reused during training.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation The CMSA captions, MDIM interests, and SID targets are cached offline and reused during training

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.669856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 86dba087-7928-4f34-9347-63b30ca745e1 · outbound

This paper cites Bright yellow and white soccer ball with traditional panel design.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation Bright yellow and white soccer ball with traditional panel design

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.664897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1db11a4f-58b6-41d1-b46b-775dcc03e811 · outbound

This paper cites soccer ball.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation soccer ball

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.659590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b3f4d534-00a6-4675-bf14-52cc7dbf334d · outbound

This paper cites This enriched representation ensures that the resulting SID carries both explicit attributes and latent user motivations.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation This enriched representation ensures that the resulting SID carries both explicit attributes and latent user motivations

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.661276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa46abd3-fe94-42c4-83d2-3c98da7d23ed · outbound

This paper cites This case study demonstrates how MDIM and CMSA work synergistically to addressSID Content Degradation (SCD)by enriching item representations before quantization.

TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation This case study demonstrates how MDIM and CMSA work synergistically to addressSID Content Degradation (SCD)by enriching item representations before quantization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:13:35.663106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation bda9be97-5eb4-415e-92fb-0e4c95a6e44f · inbound

Meta-Modal Agent: Sequential Evidence Routing for Missing-Modality Candidate Reranking cites this paper.

Meta-Modal Agent: Sequential Evidence Routing for Missing-Modality Candidate Reranking TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-06-29T23:54:03.373184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.